Residual Learning is a neural network design paradigm that introduces shortcut connections to effectively bypass one or more layers, allowing the model to learn residual mappings instead of direct mappings. This approach addresses the degradation problem in deep networks, enabling the training of much deeper networks by facilitating easier gradient flow during backpropagation.